How to Intermediate · 3 min read

Computer use latency optimization

Q: Computer use latency optimization

To optimize latency in computer_use with OpenAI's API, use asynchronous calls and streaming responses to reduce wait times. Also, minimize payload size by sending concise messages and leverage caching or local computation when possible.

Quick answer

To optimize latency in computer_use with OpenAI's API, use asynchronous calls and streaming responses to reduce wait times. Also, minimize payload size by sending concise messages and leverage caching or local computation when possible.

PREREQUISITES

Python 3.8+
OpenAI API key (free tier works)
pip install openai>=1.0

Setup

Install the latest openai Python SDK and set your API key as an environment variable for secure authentication.

bash

pip install --upgrade openai

output

Collecting openai
  Downloading openai-1.x.x-py3-none-any.whl (xx kB)
Installing collected packages: openai
Successfully installed openai-1.x.x

Step by step

Use the OpenAI client with computer-use-2024-10-22 beta enabled and asynchronous streaming to reduce latency. The example below demonstrates a minimal working script that sends a request to take a screenshot and streams the response.

python

import os
import asyncio
from openai import OpenAI

async def main():
    client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
    
    # Asynchronous streaming call with computer use beta
    stream = await client.chat.completions.acreate(
        model="claude-3-5-sonnet-20241022",
        betas=["computer-use-2024-10-22"],
        tools=[{"type": "computer_20241022", "name": "computer", "display_width_px": 1024, "display_height_px": 768}],
        messages=[{"role": "user", "content": "Take a screenshot"}],
        stream=True
    )

    print("Streaming response:")
    async for chunk in stream:
        delta = chunk.choices[0].delta.content or ""
        print(delta, end="", flush=True)

if __name__ == "__main__":
    asyncio.run(main())

output

Streaming response:
[Screenshot captured and saved to clipboard]

Common variations

Use synchronous calls if async is not needed, but expect higher latency.
Adjust display_width_px and display_height_px to optimize payload size.
Use smaller models like claude-3-5-haiku-20241022 for faster responses if high fidelity is not required.

python

from openai import OpenAI
import os

client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])

response = client.chat.completions.create(
    model="claude-3-5-haiku-20241022",
    betas=["computer-use-2024-10-22"],
    tools=[{"type": "computer_20241022", "name": "computer", "display_width_px": 800, "display_height_px": 600}],
    messages=[{"role": "user", "content": "List files in current directory"}]
)
print(response.choices[0].message.content)

output

file1.txt
file2.py
README.md

Troubleshooting

If you receive an authentication error, verify your OPENAI_API_KEY environment variable is set correctly.
If streaming hangs, check your network connection and retry the request.
For unexpected tool call failures, ensure betas includes "computer-use-2024-10-22" and the tools parameter is correctly formatted.

✅

Key Takeaways

Use asynchronous streaming calls to minimize latency in computer use tasks.
Keep tool parameters concise to reduce payload size and speed up responses.
Always include the correct beta flag and tool type for computer use features.
Adjust model and display resolution settings based on latency and fidelity needs.

Verified 2026-04 · claude-3-5-sonnet-20241022, claude-3-5-haiku-20241022

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